Overview

Dataset statistics

Number of variables20
Number of observations2059
Missing cells745
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory321.8 KiB
Average record size in memory160.1 B

Variable types

Categorical7
Text5
Numeric8

Alerts

Drivetrain is highly overall correlated with Fuel Tank Capacity and 2 other fieldsHigh correlation
Fuel Tank Capacity is highly overall correlated with Drivetrain and 4 other fieldsHigh correlation
Height is highly overall correlated with Make and 1 other fieldsHigh correlation
Kilometer is highly overall correlated with YearHigh correlation
Length is highly overall correlated with Fuel Tank Capacity and 3 other fieldsHigh correlation
Make is highly overall correlated with Drivetrain and 3 other fieldsHigh correlation
Price is highly overall correlated with Fuel Tank Capacity and 4 other fieldsHigh correlation
Seating Capacity is highly overall correlated with HeightHigh correlation
Transmission is highly overall correlated with Fuel Tank Capacity and 3 other fieldsHigh correlation
Width is highly overall correlated with Drivetrain and 4 other fieldsHigh correlation
Year is highly overall correlated with Kilometer and 1 other fieldsHigh correlation
Fuel Type is highly imbalanced (61.5%)Imbalance
Owner is highly imbalanced (64.4%)Imbalance
Seller Type is highly imbalanced (86.9%)Imbalance
Engine has 80 (3.9%) missing valuesMissing
Max Power has 80 (3.9%) missing valuesMissing
Max Torque has 80 (3.9%) missing valuesMissing
Drivetrain has 136 (6.6%) missing valuesMissing
Length has 64 (3.1%) missing valuesMissing
Width has 64 (3.1%) missing valuesMissing
Height has 64 (3.1%) missing valuesMissing
Seating Capacity has 64 (3.1%) missing valuesMissing
Fuel Tank Capacity has 113 (5.5%) missing valuesMissing
Kilometer is highly skewed (γ1 = 20.98071903)Skewed

Reproduction

Analysis started2025-12-07 05:44:26.278254
Analysis finished2025-12-07 05:45:31.745143
Duration1 minute and 5.47 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Make
Categorical

High correlation 

Distinct33
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
Maruti Suzuki
440 
Hyundai
349 
Mercedes-Benz
171 
Honda
158 
Toyota
132 
Other values (28)
809 

Length

Max length13
Median length10
Mean length7.9791161
Min length2

Characters and Unicode

Total characters16429
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowHonda
2nd rowMaruti Suzuki
3rd rowHyundai
4th rowToyota
5th rowToyota

Common Values

ValueCountFrequency (%)
Maruti Suzuki440
21.4%
Hyundai349
16.9%
Mercedes-Benz171
 
8.3%
Honda158
 
7.7%
Toyota132
 
6.4%
Audi127
 
6.2%
BMW121
 
5.9%
Mahindra119
 
5.8%
Tata57
 
2.8%
Volkswagen50
 
2.4%
Other values (23)335
16.3%

Length

2025-12-07T11:15:32.694201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maruti440
17.4%
suzuki440
17.4%
hyundai349
13.8%
mercedes-benz171
 
6.8%
honda158
 
6.2%
toyota132
 
5.2%
audi127
 
5.0%
bmw121
 
4.8%
mahindra119
 
4.7%
tata57
 
2.3%
Other values (25)418
16.5%

Most occurring characters

ValueCountFrequency (%)
u1878
 
11.4%
a1687
 
10.3%
i1532
 
9.3%
d1045
 
6.4%
n954
 
5.8%
e886
 
5.4%
M884
 
5.4%
r855
 
5.2%
t694
 
4.2%
o657
 
4.0%
Other values (34)5357
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)16429
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u1878
 
11.4%
a1687
 
10.3%
i1532
 
9.3%
d1045
 
6.4%
n954
 
5.8%
e886
 
5.4%
M884
 
5.4%
r855
 
5.2%
t694
 
4.2%
o657
 
4.0%
Other values (34)5357
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16429
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u1878
 
11.4%
a1687
 
10.3%
i1532
 
9.3%
d1045
 
6.4%
n954
 
5.8%
e886
 
5.4%
M884
 
5.4%
r855
 
5.2%
t694
 
4.2%
o657
 
4.0%
Other values (34)5357
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16429
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u1878
 
11.4%
a1687
 
10.3%
i1532
 
9.3%
d1045
 
6.4%
n954
 
5.8%
e886
 
5.4%
M884
 
5.4%
r855
 
5.2%
t694
 
4.2%
o657
 
4.0%
Other values (34)5357
32.6%

Model
Text

Distinct1050
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
2025-12-07T11:15:35.586223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length38
Mean length21.155415
Min length4

Characters and Unicode

Total characters43559
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique613 ?
Unique (%)29.8%

Sample

1st rowAmaze 1.2 VX i-VTEC
2nd rowSwift DZire VDI
3rd rowi10 Magna 1.2 Kappa2
4th rowGlanza G
5th rowInnova 2.4 VX 7 STR [2016-2020]
ValueCountFrequency (%)
at247
 
2.9%
1.2176
 
2.1%
plus170
 
2.0%
petrol134
 
1.6%
sx133
 
1.6%
vxi131
 
1.5%
tdi123
 
1.4%
1.6120
 
1.4%
diesel115
 
1.4%
o107
 
1.3%
Other values (650)7038
82.9%
2025-12-07T11:15:39.177143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6439
 
14.8%
i2039
 
4.7%
e1885
 
4.3%
01806
 
4.1%
21765
 
4.1%
r1651
 
3.8%
a1546
 
3.5%
o1442
 
3.3%
11369
 
3.1%
t1347
 
3.1%
Other values (62)22270
51.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)43559
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6439
 
14.8%
i2039
 
4.7%
e1885
 
4.3%
01806
 
4.1%
21765
 
4.1%
r1651
 
3.8%
a1546
 
3.5%
o1442
 
3.3%
11369
 
3.1%
t1347
 
3.1%
Other values (62)22270
51.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)43559
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6439
 
14.8%
i2039
 
4.7%
e1885
 
4.3%
01806
 
4.1%
21765
 
4.1%
r1651
 
3.8%
a1546
 
3.5%
o1442
 
3.3%
11369
 
3.1%
t1347
 
3.1%
Other values (62)22270
51.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)43559
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6439
 
14.8%
i2039
 
4.7%
e1885
 
4.3%
01806
 
4.1%
21765
 
4.1%
r1651
 
3.8%
a1546
 
3.5%
o1442
 
3.3%
11369
 
3.1%
t1347
 
3.1%
Other values (62)22270
51.1%

Price
Real number (ℝ)

High correlation 

Distinct619
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1702991.7
Minimum49000
Maximum35000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:15:39.925878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum49000
5-th percentile250000
Q1484999
median825000
Q31925000
95-th percentile5900000
Maximum35000000
Range34951000
Interquartile range (IQR)1440001

Descriptive statistics

Standard deviation2419880.6
Coefficient of variation (CV)1.4209586
Kurtosis40.298155
Mean1702991.7
Median Absolute Deviation (MAD)465000
Skewness4.9651429
Sum3.5064599 × 109
Variance5.8558223 × 1012
MonotonicityNot monotonic
2025-12-07T11:15:40.727413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42500026
 
1.3%
62500024
 
1.2%
65000022
 
1.1%
45000020
 
1.0%
37500020
 
1.0%
67500019
 
0.9%
55000019
 
0.9%
25000019
 
0.9%
32500018
 
0.9%
72500016
 
0.8%
Other values (609)1856
90.1%
ValueCountFrequency (%)
490001
 
< 0.1%
710011
 
< 0.1%
1000001
 
< 0.1%
1149991
 
< 0.1%
1200002
0.1%
1300002
0.1%
1350001
 
< 0.1%
1400001
 
< 0.1%
1410001
 
< 0.1%
1450003
0.1%
ValueCountFrequency (%)
350000001
 
< 0.1%
275000001
 
< 0.1%
240000001
 
< 0.1%
220000001
 
< 0.1%
200000003
0.1%
193000001
 
< 0.1%
185000002
0.1%
180000001
 
< 0.1%
162000001
 
< 0.1%
149000001
 
< 0.1%

Year
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.4254
Minimum1988
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:15:41.337660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1988
5-th percentile2011
Q12014
median2017
Q32019
95-th percentile2021
Maximum2022
Range34
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3635636
Coefficient of variation (CV)0.0016680823
Kurtosis2.9266435
Mean2016.4254
Median Absolute Deviation (MAD)2
Skewness-0.84068453
Sum4151820
Variance11.31356
MonotonicityNot monotonic
2025-12-07T11:15:42.010630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2018268
13.0%
2017262
12.7%
2019218
10.6%
2014192
9.3%
2016187
9.1%
2015178
8.6%
2021156
7.6%
2020132
6.4%
2013128
6.2%
201292
 
4.5%
Other values (12)246
11.9%
ValueCountFrequency (%)
19881
 
< 0.1%
19961
 
< 0.1%
20001
 
< 0.1%
20021
 
< 0.1%
20041
 
< 0.1%
20062
 
0.1%
20076
 
0.3%
200813
 
0.6%
200933
1.6%
201027
1.3%
ValueCountFrequency (%)
202281
 
3.9%
2021156
7.6%
2020132
6.4%
2019218
10.6%
2018268
13.0%
2017262
12.7%
2016187
9.1%
2015178
8.6%
2014192
9.3%
2013128
6.2%

Kilometer
Real number (ℝ)

High correlation  Skewed 

Distinct847
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54224.714
Minimum0
Maximum2000000
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:15:42.918248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8000
Q129000
median50000
Q372000
95-th percentile107991.1
Maximum2000000
Range2000000
Interquartile range (IQR)43000

Descriptive statistics

Standard deviation57361.721
Coefficient of variation (CV)1.057852
Kurtosis669.60863
Mean54224.714
Median Absolute Deviation (MAD)22000
Skewness20.980719
Sum1.1164869 × 108
Variance3.2903671 × 109
MonotonicityNot monotonic
2025-12-07T11:15:44.089653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6500033
 
1.6%
7200032
 
1.6%
7500029
 
1.4%
4500029
 
1.4%
5000029
 
1.4%
4200028
 
1.4%
5500026
 
1.3%
7000026
 
1.3%
3500022
 
1.1%
3800021
 
1.0%
Other values (837)1784
86.6%
ValueCountFrequency (%)
01
< 0.1%
12
0.1%
751
< 0.1%
5001
< 0.1%
6001
< 0.1%
10001
< 0.1%
11021
< 0.1%
13002
0.1%
13741
< 0.1%
15001
< 0.1%
ValueCountFrequency (%)
20000001
< 0.1%
9250001
< 0.1%
4400001
< 0.1%
2612361
< 0.1%
2400001
< 0.1%
2220001
< 0.1%
2190001
< 0.1%
2110001
< 0.1%
1950001
< 0.1%
1923261
< 0.1%

Fuel Type
Categorical

Imbalance 

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
Diesel
1049 
Petrol
942 
CNG
 
50
Electric
 
7
LPG
 
5
Other values (4)
 
6

Length

Max length12
Median length6
Mean length5.9339485
Min length3

Characters and Unicode

Total characters12218
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowPetrol
2nd rowDiesel
3rd rowPetrol
4th rowPetrol
5th rowDiesel

Common Values

ValueCountFrequency (%)
Diesel1049
50.9%
Petrol942
45.8%
CNG50
 
2.4%
Electric7
 
0.3%
LPG5
 
0.2%
Hybrid3
 
0.1%
CNG + CNG1
 
< 0.1%
Petrol + CNG1
 
< 0.1%
Petrol + LPG1
 
< 0.1%

Length

2025-12-07T11:15:44.921952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T11:15:45.539765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
diesel1049
50.8%
petrol944
45.7%
cng53
 
2.6%
electric7
 
0.3%
lpg6
 
0.3%
hybrid3
 
0.1%
3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e3049
25.0%
l2000
16.4%
i1059
 
8.7%
D1049
 
8.6%
s1049
 
8.6%
r954
 
7.8%
t951
 
7.8%
P950
 
7.8%
o944
 
7.7%
G59
 
0.5%
Other values (11)154
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)12218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e3049
25.0%
l2000
16.4%
i1059
 
8.7%
D1049
 
8.6%
s1049
 
8.6%
r954
 
7.8%
t951
 
7.8%
P950
 
7.8%
o944
 
7.7%
G59
 
0.5%
Other values (11)154
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e3049
25.0%
l2000
16.4%
i1059
 
8.7%
D1049
 
8.6%
s1049
 
8.6%
r954
 
7.8%
t951
 
7.8%
P950
 
7.8%
o944
 
7.7%
G59
 
0.5%
Other values (11)154
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e3049
25.0%
l2000
16.4%
i1059
 
8.7%
D1049
 
8.6%
s1049
 
8.6%
r954
 
7.8%
t951
 
7.8%
P950
 
7.8%
o944
 
7.7%
G59
 
0.5%
Other values (11)154
 
1.3%

Transmission
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
Manual
1133 
Automatic
926 

Length

Max length9
Median length6
Mean length7.3491986
Min length6

Characters and Unicode

Total characters15132
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowManual

Common Values

ValueCountFrequency (%)
Manual1133
55.0%
Automatic926
45.0%

Length

2025-12-07T11:15:46.320860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T11:15:46.813470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
manual1133
55.0%
automatic926
45.0%

Most occurring characters

ValueCountFrequency (%)
a3192
21.1%
u2059
13.6%
t1852
12.2%
M1133
 
7.5%
n1133
 
7.5%
l1133
 
7.5%
A926
 
6.1%
o926
 
6.1%
m926
 
6.1%
i926
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)15132
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a3192
21.1%
u2059
13.6%
t1852
12.2%
M1133
 
7.5%
n1133
 
7.5%
l1133
 
7.5%
A926
 
6.1%
o926
 
6.1%
m926
 
6.1%
i926
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15132
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a3192
21.1%
u2059
13.6%
t1852
12.2%
M1133
 
7.5%
n1133
 
7.5%
l1133
 
7.5%
A926
 
6.1%
o926
 
6.1%
m926
 
6.1%
i926
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15132
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a3192
21.1%
u2059
13.6%
t1852
12.2%
M1133
 
7.5%
n1133
 
7.5%
l1133
 
7.5%
A926
 
6.1%
o926
 
6.1%
m926
 
6.1%
i926
 
6.1%
Distinct77
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
2025-12-07T11:15:48.181891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length11
Mean length6.7304517
Min length3

Characters and Unicode

Total characters13858
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st rowPune
2nd rowLudhiana
3rd rowLucknow
4th rowMangalore
5th rowMumbai
ValueCountFrequency (%)
mumbai361
17.2%
delhi307
14.7%
pune144
 
6.9%
bangalore132
 
6.3%
hyderabad116
 
5.5%
lucknow78
 
3.7%
ahmedabad70
 
3.3%
chennai63
 
3.0%
kolkata60
 
2.9%
kanpur52
 
2.5%
Other values (70)711
34.0%
2025-12-07T11:15:50.292149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a2299
16.6%
i1100
 
7.9%
e982
 
7.1%
u898
 
6.5%
n892
 
6.4%
h751
 
5.4%
r725
 
5.2%
d636
 
4.6%
l634
 
4.6%
b631
 
4.6%
Other values (39)4310
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)13858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a2299
16.6%
i1100
 
7.9%
e982
 
7.1%
u898
 
6.5%
n892
 
6.4%
h751
 
5.4%
r725
 
5.2%
d636
 
4.6%
l634
 
4.6%
b631
 
4.6%
Other values (39)4310
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a2299
16.6%
i1100
 
7.9%
e982
 
7.1%
u898
 
6.5%
n892
 
6.4%
h751
 
5.4%
r725
 
5.2%
d636
 
4.6%
l634
 
4.6%
b631
 
4.6%
Other values (39)4310
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a2299
16.6%
i1100
 
7.9%
e982
 
7.1%
u898
 
6.5%
n892
 
6.4%
h751
 
5.4%
r725
 
5.2%
d636
 
4.6%
l634
 
4.6%
b631
 
4.6%
Other values (39)4310
31.1%

Color
Categorical

Distinct17
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
White
802 
Silver
285 
Grey
220 
Blue
190 
Black
163 
Other values (12)
399 

Length

Max length6
Median length5
Mean length4.8266149
Min length3

Characters and Unicode

Total characters9938
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowGrey
2nd rowWhite
3rd rowMaroon
4th rowRed
5th rowGrey

Common Values

ValueCountFrequency (%)
White802
39.0%
Silver285
 
13.8%
Grey220
 
10.7%
Blue190
 
9.2%
Black163
 
7.9%
Red154
 
7.5%
Brown82
 
4.0%
Maroon37
 
1.8%
Gold30
 
1.5%
Bronze28
 
1.4%
Other values (7)68
 
3.3%

Length

2025-12-07T11:15:50.962879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
white802
39.0%
silver285
 
13.8%
grey220
 
10.7%
blue190
 
9.2%
black163
 
7.9%
red154
 
7.5%
brown82
 
4.0%
maroon37
 
1.8%
gold30
 
1.5%
bronze28
 
1.4%
Other values (7)68
 
3.3%

Most occurring characters

ValueCountFrequency (%)
e1771
17.8%
i1096
11.0%
t814
 
8.2%
h814
 
8.2%
W802
 
8.1%
r702
 
7.1%
l691
 
7.0%
B471
 
4.7%
S285
 
2.9%
v285
 
2.9%
Other values (19)2207
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)9938
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1771
17.8%
i1096
11.0%
t814
 
8.2%
h814
 
8.2%
W802
 
8.1%
r702
 
7.1%
l691
 
7.0%
B471
 
4.7%
S285
 
2.9%
v285
 
2.9%
Other values (19)2207
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9938
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1771
17.8%
i1096
11.0%
t814
 
8.2%
h814
 
8.2%
W802
 
8.1%
r702
 
7.1%
l691
 
7.0%
B471
 
4.7%
S285
 
2.9%
v285
 
2.9%
Other values (19)2207
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9938
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1771
17.8%
i1096
11.0%
t814
 
8.2%
h814
 
8.2%
W802
 
8.1%
r702
 
7.1%
l691
 
7.0%
B471
 
4.7%
S285
 
2.9%
v285
 
2.9%
Other values (19)2207
22.2%

Owner
Categorical

Imbalance 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
First
1619 
Second
373 
Third
 
42
UnRegistered Car
 
21
Fourth
 
3

Length

Max length16
Median length5
Mean length5.296746
Min length5

Characters and Unicode

Total characters10906
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFirst
2nd rowSecond
3rd rowFirst
4th rowFirst
5th rowFirst

Common Values

ValueCountFrequency (%)
First1619
78.6%
Second373
 
18.1%
Third42
 
2.0%
UnRegistered Car21
 
1.0%
Fourth3
 
0.1%
4 or More1
 
< 0.1%

Length

2025-12-07T11:15:51.616170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T11:15:52.163620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
first1619
77.8%
second373
 
17.9%
third42
 
2.0%
unregistered21
 
1.0%
car21
 
1.0%
fourth3
 
0.1%
41
 
< 0.1%
or1
 
< 0.1%
more1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r1708
15.7%
i1682
15.4%
t1643
15.1%
s1640
15.0%
F1622
14.9%
e437
 
4.0%
d436
 
4.0%
n394
 
3.6%
o378
 
3.5%
S373
 
3.4%
Other values (12)593
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10906
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r1708
15.7%
i1682
15.4%
t1643
15.1%
s1640
15.0%
F1622
14.9%
e437
 
4.0%
d436
 
4.0%
n394
 
3.6%
o378
 
3.5%
S373
 
3.4%
Other values (12)593
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10906
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r1708
15.7%
i1682
15.4%
t1643
15.1%
s1640
15.0%
F1622
14.9%
e437
 
4.0%
d436
 
4.0%
n394
 
3.6%
o378
 
3.5%
S373
 
3.4%
Other values (12)593
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10906
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r1708
15.7%
i1682
15.4%
t1643
15.1%
s1640
15.0%
F1622
14.9%
e437
 
4.0%
d436
 
4.0%
n394
 
3.6%
o378
 
3.5%
S373
 
3.4%
Other values (12)593
 
5.4%

Seller Type
Categorical

Imbalance 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.2 KiB
Individual
1997 
Corporate
 
57
Commercial Registration
 
5

Length

Max length23
Median length10
Mean length10.003885
Min length9

Characters and Unicode

Total characters20598
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCorporate
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual1997
97.0%
Corporate57
 
2.8%
Commercial Registration5
 
0.2%

Length

2025-12-07T11:15:52.920294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T11:15:53.383794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
individual1997
96.8%
corporate57
 
2.8%
commercial5
 
0.2%
registration5
 
0.2%

Most occurring characters

ValueCountFrequency (%)
i4009
19.5%
d3994
19.4%
a2064
10.0%
l2002
9.7%
n2002
9.7%
I1997
9.7%
v1997
9.7%
u1997
9.7%
o124
 
0.6%
r124
 
0.6%
Other values (10)288
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)20598
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i4009
19.5%
d3994
19.4%
a2064
10.0%
l2002
9.7%
n2002
9.7%
I1997
9.7%
v1997
9.7%
u1997
9.7%
o124
 
0.6%
r124
 
0.6%
Other values (10)288
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20598
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i4009
19.5%
d3994
19.4%
a2064
10.0%
l2002
9.7%
n2002
9.7%
I1997
9.7%
v1997
9.7%
u1997
9.7%
o124
 
0.6%
r124
 
0.6%
Other values (10)288
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20598
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i4009
19.5%
d3994
19.4%
a2064
10.0%
l2002
9.7%
n2002
9.7%
I1997
9.7%
v1997
9.7%
u1997
9.7%
o124
 
0.6%
r124
 
0.6%
Other values (10)288
 
1.4%

Engine
Text

Missing 

Distinct108
Distinct (%)5.5%
Missing80
Missing (%)3.9%
Memory size16.2 KiB
2025-12-07T11:15:54.840228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.8943911
Min length6

Characters and Unicode

Total characters13644
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)1.0%

Sample

1st row1198 cc
2nd row1248 cc
3rd row1197 cc
4th row1197 cc
5th row2393 cc
ValueCountFrequency (%)
cc1979
50.0%
1197231
 
5.8%
1248122
 
3.1%
998121
 
3.1%
149784
 
2.1%
196882
 
2.1%
199582
 
2.1%
217973
 
1.8%
149864
 
1.6%
158256
 
1.4%
Other values (99)1064
26.9%
2025-12-07T11:15:56.850438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c3958
29.0%
92039
14.9%
11983
14.5%
1979
14.5%
8729
 
5.3%
2626
 
4.6%
7601
 
4.4%
4560
 
4.1%
5395
 
2.9%
6353
 
2.6%
Other values (2)421
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)13644
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c3958
29.0%
92039
14.9%
11983
14.5%
1979
14.5%
8729
 
5.3%
2626
 
4.6%
7601
 
4.4%
4560
 
4.1%
5395
 
2.9%
6353
 
2.6%
Other values (2)421
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13644
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c3958
29.0%
92039
14.9%
11983
14.5%
1979
14.5%
8729
 
5.3%
2626
 
4.6%
7601
 
4.4%
4560
 
4.1%
5395
 
2.9%
6353
 
2.6%
Other values (2)421
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13644
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c3958
29.0%
92039
14.9%
11983
14.5%
1979
14.5%
8729
 
5.3%
2626
 
4.6%
7601
 
4.4%
4560
 
4.1%
5395
 
2.9%
6353
 
2.6%
Other values (2)421
 
3.1%

Max Power
Text

Missing 

Distinct335
Distinct (%)16.9%
Missing80
Missing (%)3.9%
Memory size16.2 KiB
2025-12-07T11:15:58.888297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length18
Mean length16.915109
Min length7

Characters and Unicode

Total characters33475
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique117 ?
Unique (%)5.9%

Sample

1st row87 bhp @ 6000 rpm
2nd row74 bhp @ 4000 rpm
3rd row79 bhp @ 6000 rpm
4th row82 bhp @ 6000 rpm
5th row148 bhp @ 3400 rpm
ValueCountFrequency (%)
1851
19.7%
bhp1851
19.7%
rpm1847
19.7%
4000431
 
4.6%
6000422
 
4.5%
3750143
 
1.5%
89139
 
1.5%
550097
 
1.0%
360092
 
1.0%
6789
 
0.9%
Other values (228)2414
25.7%
2025-12-07T11:16:01.970680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7397
22.1%
05016
15.0%
p3698
11.0%
@1979
 
5.9%
b1851
 
5.5%
h1851
 
5.5%
r1847
 
5.5%
m1847
 
5.5%
11301
 
3.9%
61178
 
3.5%
Other values (8)5510
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)33475
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7397
22.1%
05016
15.0%
p3698
11.0%
@1979
 
5.9%
b1851
 
5.5%
h1851
 
5.5%
r1847
 
5.5%
m1847
 
5.5%
11301
 
3.9%
61178
 
3.5%
Other values (8)5510
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)33475
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7397
22.1%
05016
15.0%
p3698
11.0%
@1979
 
5.9%
b1851
 
5.5%
h1851
 
5.5%
r1847
 
5.5%
m1847
 
5.5%
11301
 
3.9%
61178
 
3.5%
Other values (8)5510
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)33475
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7397
22.1%
05016
15.0%
p3698
11.0%
@1979
 
5.9%
b1851
 
5.5%
h1851
 
5.5%
r1847
 
5.5%
m1847
 
5.5%
11301
 
3.9%
61178
 
3.5%
Other values (8)5510
16.5%

Max Torque
Text

Missing 

Distinct290
Distinct (%)14.7%
Missing80
Missing (%)3.9%
Memory size16.2 KiB
2025-12-07T11:16:03.936954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length17
Mean length16.371905
Min length7

Characters and Unicode

Total characters32400
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)4.6%

Sample

1st row109 Nm @ 4500 rpm
2nd row190 Nm @ 2000 rpm
3rd row112.7619 Nm @ 4000 rpm
4th row113 Nm @ 4200 rpm
5th row343 Nm @ 1400 rpm
ValueCountFrequency (%)
1851
19.7%
rpm1851
19.7%
nm1851
19.7%
1750382
 
4.1%
4000240
 
2.6%
1500202
 
2.2%
1600160
 
1.7%
3500128
 
1.4%
200121
 
1.3%
400113
 
1.2%
Other values (219)2485
26.5%
2025-12-07T11:16:06.638636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7405
22.9%
05432
16.8%
m3702
11.4%
12084
 
6.4%
@1979
 
6.1%
p1851
 
5.7%
N1851
 
5.7%
r1851
 
5.7%
51460
 
4.5%
41215
 
3.8%
Other values (7)3570
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)32400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7405
22.9%
05432
16.8%
m3702
11.4%
12084
 
6.4%
@1979
 
6.1%
p1851
 
5.7%
N1851
 
5.7%
r1851
 
5.7%
51460
 
4.5%
41215
 
3.8%
Other values (7)3570
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)32400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7405
22.9%
05432
16.8%
m3702
11.4%
12084
 
6.4%
@1979
 
6.1%
p1851
 
5.7%
N1851
 
5.7%
r1851
 
5.7%
51460
 
4.5%
41215
 
3.8%
Other values (7)3570
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)32400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7405
22.9%
05432
16.8%
m3702
11.4%
12084
 
6.4%
@1979
 
6.1%
p1851
 
5.7%
N1851
 
5.7%
r1851
 
5.7%
51460
 
4.5%
41215
 
3.8%
Other values (7)3570
11.0%

Drivetrain
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.2%
Missing136
Missing (%)6.6%
Memory size16.2 KiB
FWD
1330 
RWD
321 
AWD
272 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5769
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFWD
2nd rowFWD
3rd rowFWD
4th rowFWD
5th rowRWD

Common Values

ValueCountFrequency (%)
FWD1330
64.6%
RWD321
 
15.6%
AWD272
 
13.2%
(Missing)136
 
6.6%

Length

2025-12-07T11:16:07.178911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-07T11:16:07.635474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fwd1330
69.2%
rwd321
 
16.7%
awd272
 
14.1%

Most occurring characters

ValueCountFrequency (%)
W1923
33.3%
D1923
33.3%
F1330
23.1%
R321
 
5.6%
A272
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)5769
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W1923
33.3%
D1923
33.3%
F1330
23.1%
R321
 
5.6%
A272
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5769
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W1923
33.3%
D1923
33.3%
F1330
23.1%
R321
 
5.6%
A272
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5769
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W1923
33.3%
D1923
33.3%
F1330
23.1%
R321
 
5.6%
A272
 
4.7%

Length
Real number (ℝ)

High correlation  Missing 

Distinct248
Distinct (%)12.4%
Missing64
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean4280.8607
Minimum3099
Maximum5569
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:16:08.357905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3099
5-th percentile3565
Q13985
median4370
Q34629
95-th percentile4936
Maximum5569
Range2470
Interquartile range (IQR)644

Descriptive statistics

Standard deviation442.45851
Coefficient of variation (CV)0.10335737
Kurtosis-0.82045722
Mean4280.8607
Median Absolute Deviation (MAD)375
Skewness-0.02152263
Sum8540317
Variance195769.53
MonotonicityNot monotonic
2025-12-07T11:16:09.319394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3995221
 
10.7%
444070
 
3.4%
427057
 
2.8%
398555
 
2.7%
445644
 
2.1%
385042
 
2.0%
376540
 
1.9%
458539
 
1.9%
449036
 
1.7%
473534
 
1.7%
Other values (238)1357
65.9%
(Missing)64
 
3.1%
ValueCountFrequency (%)
30991
 
< 0.1%
339520
1.0%
34294
 
0.2%
34452
 
0.1%
349523
1.1%
35154
 
0.2%
35204
 
0.2%
353913
0.6%
354511
0.5%
356525
1.2%
ValueCountFrequency (%)
55692
 
0.1%
54621
 
< 0.1%
54531
 
< 0.1%
53991
 
< 0.1%
52651
 
< 0.1%
52551
 
< 0.1%
52523
0.1%
52471
 
< 0.1%
52465
0.2%
52263
0.1%

Width
Real number (ℝ)

High correlation  Missing 

Distinct170
Distinct (%)8.5%
Missing64
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean1767.992
Minimum1475
Maximum2220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:16:10.216711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1475
5-th percentile1495
Q11695
median1770
Q31831.5
95-th percentile2044
Maximum2220
Range745
Interquartile range (IQR)136.5

Descriptive statistics

Standard deviation135.26583
Coefficient of variation (CV)0.076508167
Kurtosis0.89759088
Mean1767.992
Median Absolute Deviation (MAD)75
Skewness0.30833494
Sum3527144
Variance18296.843
MonotonicityNot monotonic
2025-12-07T11:16:11.163158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1695201
 
9.8%
178062
 
3.0%
179061
 
3.0%
166051
 
2.5%
168051
 
2.5%
189049
 
2.4%
147549
 
2.4%
182046
 
2.2%
174546
 
2.2%
173444
 
2.1%
Other values (160)1335
64.8%
(Missing)64
 
3.1%
ValueCountFrequency (%)
147549
2.4%
149031
1.5%
149529
1.4%
15003
 
0.1%
15152
 
0.1%
15206
 
0.3%
15257
 
0.3%
155014
 
0.7%
15604
 
0.2%
157924
1.2%
ValueCountFrequency (%)
22204
 
0.2%
21831
 
< 0.1%
21731
 
< 0.1%
21578
0.4%
21551
 
< 0.1%
214119
0.9%
21391
 
< 0.1%
21209
0.4%
21101
 
< 0.1%
21051
 
< 0.1%

Height
Real number (ℝ)

High correlation  Missing 

Distinct196
Distinct (%)9.8%
Missing64
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean1591.7353
Minimum1165
Maximum1995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:16:12.088675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1165
5-th percentile1432
Q11485
median1545
Q31675
95-th percentile1845.3
Maximum1995
Range830
Interquartile range (IQR)190

Descriptive statistics

Standard deviation136.07396
Coefficient of variation (CV)0.085487802
Kurtosis0.034725652
Mean1591.7353
Median Absolute Deviation (MAD)79
Skewness0.83777993
Sum3175512
Variance18516.121
MonotonicityNot monotonic
2025-12-07T11:16:12.920881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147588
 
4.3%
150580
 
3.9%
153063
 
3.1%
152061
 
3.0%
149558
 
2.8%
164056
 
2.7%
150051
 
2.5%
148546
 
2.2%
155544
 
2.1%
170043
 
2.1%
Other values (186)1405
68.2%
(Missing)64
 
3.1%
ValueCountFrequency (%)
11651
< 0.1%
12131
< 0.1%
12812
0.1%
12951
< 0.1%
12971
< 0.1%
13041
< 0.1%
13531
< 0.1%
13662
0.1%
13701
< 0.1%
13912
0.1%
ValueCountFrequency (%)
199520
1.0%
197510
0.5%
19402
 
0.1%
19309
0.4%
19251
 
< 0.1%
19225
 
0.2%
19202
 
0.1%
19011
 
< 0.1%
19001
 
< 0.1%
18953
 
0.1%

Seating Capacity
Real number (ℝ)

High correlation  Missing 

Distinct6
Distinct (%)0.3%
Missing64
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean5.3062657
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:16:13.518186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q15
median5
Q35
95-th percentile7
Maximum8
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.82217013
Coefficient of variation (CV)0.15494327
Kurtosis2.5744776
Mean5.3062657
Median Absolute Deviation (MAD)0
Skewness1.4680826
Sum10586
Variance0.67596373
MonotonicityNot monotonic
2025-12-07T11:16:14.077204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
51615
78.4%
7273
 
13.3%
442
 
2.0%
835
 
1.7%
623
 
1.1%
27
 
0.3%
(Missing)64
 
3.1%
ValueCountFrequency (%)
27
 
0.3%
442
 
2.0%
51615
78.4%
623
 
1.1%
7273
 
13.3%
835
 
1.7%
ValueCountFrequency (%)
835
 
1.7%
7273
 
13.3%
623
 
1.1%
51615
78.4%
442
 
2.0%
27
 
0.3%

Fuel Tank Capacity
Real number (ℝ)

High correlation  Missing 

Distinct55
Distinct (%)2.8%
Missing113
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean52.00221
Minimum15
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.2 KiB
2025-12-07T11:16:14.900317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile35
Q141.25
median50
Q360
95-th percentile80
Maximum105
Range90
Interquartile range (IQR)18.75

Descriptive statistics

Standard deviation15.110198
Coefficient of variation (CV)0.29056838
Kurtosis0.35516399
Mean52.00221
Median Absolute Deviation (MAD)10
Skewness0.8530031
Sum101196.3
Variance228.31808
MonotonicityNot monotonic
2025-12-07T11:16:16.244138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35222
 
10.8%
60155
 
7.5%
45154
 
7.5%
43146
 
7.1%
50132
 
6.4%
55117
 
5.7%
42111
 
5.4%
40106
 
5.1%
8092
 
4.5%
3789
 
4.3%
Other values (45)622
30.2%
(Missing)113
 
5.5%
ValueCountFrequency (%)
151
 
< 0.1%
275
 
0.2%
2830
 
1.5%
3227
 
1.3%
35222
10.8%
3789
4.3%
381
 
< 0.1%
40106
5.1%
416
 
0.3%
42111
5.4%
ValueCountFrequency (%)
1053
 
0.1%
1041
 
< 0.1%
10015
0.7%
952
 
0.1%
9318
0.9%
921
 
< 0.1%
9010
0.5%
855
 
0.2%
836
 
0.3%
82.53
 
0.1%

Interactions

2025-12-07T11:15:21.091663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:32.437502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:38.471536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:45.591114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:54.493750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:01.197939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:08.433261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:14.735413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:22.091140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:33.128598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:39.906346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:47.178804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:55.534726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:01.997019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:09.220430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:15.481462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:22.792335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:33.850565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:40.643137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:48.709530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:56.212768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:02.750824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:10.339048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:16.198607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:23.551937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:34.634629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:41.426641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:49.848000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:56.976422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:03.846560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:11.236567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:17.004417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:24.240581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:35.345355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:42.166167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:50.940047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:57.661652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:04.906079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:11.968385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:17.693562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:24.980071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:36.128082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:42.962219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:51.870638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:58.376767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:06.007878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:12.655186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:18.456093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:25.734029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:36.841134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:44.169785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:52.910070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:59.433882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:06.868184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:13.290353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:19.597915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:26.473827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:37.678625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:44.818790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:14:53.757298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:00.350246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:07.656584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:13.979880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-07T11:15:20.337120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-07T11:16:16.951579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ColorDrivetrainFuel Tank CapacityFuel TypeHeightKilometerLengthMakeOwnerPriceSeating CapacitySeller TypeTransmissionWidthYear
Color1.0000.1960.1040.0720.1260.0000.0990.1390.0000.0730.1100.0000.2580.1170.192
Drivetrain0.1961.0000.5970.2890.4960.0550.4730.6260.0930.3690.3800.0000.4390.5240.050
Fuel Tank Capacity0.1040.5971.0000.2130.2620.0780.8180.4460.0520.7510.3090.0240.5460.8720.102
Fuel Type0.0720.2890.2131.0000.1820.0000.2140.2560.1160.0790.1430.0000.2060.2560.359
Height0.1260.4960.2620.1821.0000.0840.0670.5450.0620.1270.5660.0750.3220.2810.142
Kilometer0.0000.0550.0780.0000.0841.0000.0550.0000.000-0.2790.2020.0000.000-0.003-0.602
Length0.0990.4730.8180.2140.0670.0551.0000.4490.0780.7670.3180.0820.5720.8460.104
Make0.1390.6260.4460.2560.5450.0000.4491.0000.0870.5600.4410.1370.6780.4220.088
Owner0.0000.0930.0520.1160.0620.0000.0780.0871.0000.1090.0470.0000.0780.0560.193
Price0.0730.3690.7510.0790.127-0.2790.7670.5600.1091.0000.1370.0620.4110.8300.520
Seating Capacity0.1100.3800.3090.1430.5660.2020.3180.4410.0470.1371.0000.0000.1460.278-0.017
Seller Type0.0000.0000.0240.0000.0750.0000.0820.1370.0000.0620.0001.0000.1080.0380.000
Transmission0.2580.4390.5460.2060.3220.0000.5720.6780.0780.4110.1460.1081.0000.5780.165
Width0.1170.5240.8720.2560.281-0.0030.8460.4220.0560.8300.2780.0380.5781.0000.212
Year0.1920.0500.1020.3590.142-0.6020.1040.0880.1930.520-0.0170.0000.1650.2121.000

Missing values

2025-12-07T11:15:27.736074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-07T11:15:29.152865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-07T11:15:30.694829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

MakeModelPriceYearKilometerFuel TypeTransmissionLocationColorOwnerSeller TypeEngineMax PowerMax TorqueDrivetrainLengthWidthHeightSeating CapacityFuel Tank Capacity
0HondaAmaze 1.2 VX i-VTEC505000201787150PetrolManualPuneGreyFirstCorporate1198 cc87 bhp @ 6000 rpm109 Nm @ 4500 rpmFWD3990.0001680.0001505.0005.00035.000
1Maruti SuzukiSwift DZire VDI450000201475000DieselManualLudhianaWhiteSecondIndividual1248 cc74 bhp @ 4000 rpm190 Nm @ 2000 rpmFWD3995.0001695.0001555.0005.00042.000
2Hyundaii10 Magna 1.2 Kappa2220000201167000PetrolManualLucknowMaroonFirstIndividual1197 cc79 bhp @ 6000 rpm112.7619 Nm @ 4000 rpmFWD3585.0001595.0001550.0005.00035.000
3ToyotaGlanza G799000201937500PetrolManualMangaloreRedFirstIndividual1197 cc82 bhp @ 6000 rpm113 Nm @ 4200 rpmFWD3995.0001745.0001510.0005.00037.000
4ToyotaInnova 2.4 VX 7 STR [2016-2020]1950000201869000DieselManualMumbaiGreyFirstIndividual2393 cc148 bhp @ 3400 rpm343 Nm @ 1400 rpmRWD4735.0001830.0001795.0007.00055.000
5Maruti SuzukiCiaz ZXi675000201773315PetrolManualPuneGreyFirstIndividual1373 cc91 bhp @ 6000 rpm130 Nm @ 4000 rpmFWD4490.0001730.0001485.0005.00043.000
6Mercedes-BenzCLA 200 Petrol Sport1898999201547000PetrolAutomaticMumbaiWhiteSecondIndividual1991 cc181 bhp @ 5500 rpm300 Nm @ 1200 rpmFWD4630.0001777.0001432.0005.000NaN
7BMWX1 xDrive20d M Sport2650000201775000DieselAutomaticCoimbatoreWhiteSecondIndividual1995 cc188 bhp @ 4000 rpm400 Nm @ 1750 rpmAWD4439.0001821.0001612.0005.00051.000
8SkodaOctavia 1.8 TSI Style Plus AT [2017]1390000201756000PetrolAutomaticMumbaiWhiteFirstIndividual1798 cc177 bhp @ 5100 rpm250 Nm @ 1250 rpmFWD4670.0001814.0001476.0005.00050.000
9NissanTerrano XL (D)575000201585000DieselManualMumbaiWhiteFirstIndividual1461 cc84 bhp @ 3750 rpm200 Nm @ 1900 rpmFWD4331.0001822.0001671.0005.00050.000
MakeModelPriceYearKilometerFuel TypeTransmissionLocationColorOwnerSeller TypeEngineMax PowerMax TorqueDrivetrainLengthWidthHeightSeating CapacityFuel Tank Capacity
2049Mercedes-BenzGLS 400 4MATIC5950000201780000PetrolAutomaticDelhiBlackFirstIndividual2996 cc329 bhp @ 5250 rpm480 Nm @ 1600 rpmAWD5130.0001934.0001850.0005.000100.000
2050HyundaiCreta SX Plus 1.6 Petrol891000201647000PetrolManualDelhiWhiteFirstIndividual1591 cc122 bhp @ 6400 rpm154 Nm @ 4850 rpmFWD4270.0001780.0001630.0005.00060.000
2051Maruti SuzukiVitara Brezza VXi925000202148000PetrolManualBangaloreWhiteFirstIndividual1462 cc103 bhp @ 6000 rpm138 Nm @ 4400 rpmFWD3995.0001790.0001640.0005.00048.000
2052Hyundaii20 Sportz 1.4 CRDI409999201468000DieselManualAgraSilverFirstIndividual1396 cc90@4000220@1750NaN3940.0001710.0001505.0005.00045.000
2053Maruti SuzukiRitz Vxi (ABS) BS-IV245000201479000PetrolManualFaridabadWhiteSecondIndividual1197 cc85 bhp @ 6000 rpm113 Nm @ 4500 rpmFWD3775.0001680.0001620.0005.00043.000
2054MahindraXUV500 W8 [2015-2017]850000201690300DieselManualSuratWhiteFirstIndividual2179 cc138 bhp @ 3750 rpm330 Nm @ 1600 rpmFWD4585.0001890.0001785.0007.00070.000
2055HyundaiEon D-Lite +275000201483000PetrolManualAhmedabadWhiteSecondIndividual814 cc55 bhp @ 5500 rpm75 Nm @ 4000 rpmFWD3495.0001550.0001500.0005.00032.000
2056FordFigo Duratec Petrol ZXI 1.2240000201373000PetrolManualThaneSilverFirstIndividual1196 cc70 bhp @ 6250 rpm102 Nm @ 4000 rpmFWD3795.0001680.0001427.0005.00045.000
2057BMW5-Series 520d Luxury Line [2017-2019]4290000201860474DieselAutomaticCoimbatoreWhiteFirstIndividual1995 cc188 bhp @ 4000 rpm400 Nm @ 1750 rpmRWD4936.0001868.0001479.0005.00065.000
2058MahindraBolero Power Plus ZLX [2016-2019]670000201772000DieselManualGuwahatiWhiteFirstIndividual1493 cc70 bhp @ 3600 rpm195 Nm @ 1400 rpmRWD3995.0001745.0001880.0007.000NaN